Combining machine learning quantile regression and Gaussian random fields: a general framework for modeling and simulating space-time processes
Résumé
Modeling and simulating space-time random fields while accounting for possibly misaligned covariates is crucial for many applications requiring uncertainty quantification and risk assessment. To achieve this, we propose a flexible framework that couples machine-learning quantile regression with space-time Gaussian random fields.
In this framework, the target variable is modeled as a combination of a latent Gaussian random field and transformed marginals obtained by Machine learning quantile regression conditionally on a set of covariates. We illustrate the approach on a synthetic experiment and on a case study that considers daily maximum temperature over north-east Switzerland conditional on seasonal cycle and large-scale geopotential height.
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